国际道路安全研究的现状、挑战和趋势

Lei Han, Zhigang Du
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引用次数: 0

摘要

路边安全是指评估和改进与路边环境、设计、管理和物体有关的安全措施。它包括道路设计、标志、标线、交通控制设备和路边设施等因素,其目标是降低事故风险、减少伤害,并提高道路使用者的整体安全性和舒适性。为了全面总结道路安全研究的进展,本综述利用 Web of Science Core Collection 数据库检索了 2000 年至 2022 年间发表的 1637 篇英文论文。利用 VOSviewer 软件对文献进行了可视化分析,对论文发表情况进行了情景分析,绘制了主要研究热点和趋势的知识图谱,并总结了该领域的研究现状、方法、系统、挑战和趋势。结果显示,相关研究总体呈上升趋势。贡献最大的国家、机构和期刊分别是美国、内布拉斯加大学和《运输研究记录》。目前的研究热点包括路边安全和风险水平评估、影响路边安全和驾驶风险的因素、酒驾和毒驾与路边交通事故的关系、路边事故的频率和严重程度、路边安全保障技术和改进策略。目前的建模方法主要包括数理统计分析和基于机器学习的数据驱动建模。未来的研究应重点关注影响因素和评价标准的全面量化映射,建立基于主动引导的评价体系和优化策略,提高计算问题和模型构建的准确性,探索智能交通理论和技术在道路安全管理和改进中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Status, Challenges, and Trends of International Research on Roadside Safety
Roadside safety refers to the assessment and improvement of safety measures related to roadside environment, design, management, and objects. It encompasses factors such as road design, signage, markings, traffic control devices, and roadside features, and its goal is to reduce accident risk, minimize injuries, and enhance overall safety and comfort for road users. To comprehensively summarize roadside safety research progress, this review retrieved 1637 English papers published between 2000 and 2022, using the Web of Science Core Collection database. VOSviewer software was utilized to visualize and analyze the literature, conduct a situational analysis of publication, create knowledge maps of the main research hotspots and trends, and summarize research status, methods, systems, challenges, and trends in this field. Results showed an overall increasing trend in relevant research. The countries, institutions, and journals contributing most are the United States, the University of Nebraska, and the Transportation Research Record, respectively. Current research hotspots include evaluation of roadside safety and risk levels, factors influencing roadside safety and driving risks, drunk and drug-impaired driving in relation to roadside traffic accidents, frequency and severity of roadside accidents, and roadside safety assurance techniques and improvement strategies. Current modeling methods mainly consist of mathematical statistical analyses and data-driven modeling based on machine learning. Future research should focus on comprehensive quantitative mapping of influencing factors and evaluation criteria, establishing an active-guidance-based evaluation system and optimization strategy, improving the accuracy of computational problems and model construction, and exploring theories and technologies of intelligent transportation for roadside safety management and improvement.
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